SOTAVerified

Meta-Learning

Meta-learning is a methodology considered with "learning to learn" machine learning algorithms.

( Image credit: Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2221–2230 of 3569 papers

TitleStatusHype
A Survey on Curriculum Learning—0
A Comprehensive Survey of Dataset Distillation—0
A Comprehensive Sustainable Framework for Machine Learning and Artificial Intelligence—0
A Concise Review of Recent Few-shot Meta-learning Methods—0
A Primal-Dual Approach to Bilevel Optimization with Multiple Inner Minima—0
Acquiring and Adapting Priors for Novel Tasks via Neural Meta-Architectures—0
A Cross-Lingual Meta-Learning Method Based on Domain Adaptation for Speech Emotion Recognition—0
Across-Task Neural Architecture Search via Meta Learning—0
Actively Seeking and Learning from Live Data—0
Adaptable Text Matching via Meta-Weight Regulator—0
Show:102550
← PrevPage 223 of 357Next →
All datasetsML10MT50ML45OMNIGLOT - 1-Shot, 20-way

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-train success rate97.8—Unverified
2MZMeta-train success rate97.6—Unverified
3MAMLMeta-test success rate36—Unverified
4RL^2Meta-test success rate10—Unverified
5DnCMeta-test success rate5.4—Unverified
6PEARLMeta-test success rate0—Unverified
#ModelMetricClaimedVerifiedStatus
1SoftModuleAverage Success Rate60—Unverified
2Multi-task multi-head SACAverage Success Rate35.85—Unverified
3DisCorAverage Success Rate26—Unverified
4NDPAverage Success Rate11—Unverified
#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-test success rate (zero-shot)18.5—Unverified
2MZMeta-test success rate (zero-shot)17.7—Unverified
#ModelMetricClaimedVerifiedStatus
1Metadrop% Test Accuracy95.75—Unverified